Guiding a Harsh-Environments Robust Detector via RAW Data Characteristic Mining
Authors: Hongyang Chen, Hung-Shuo Tai, Kaisheng Ma
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Specifically, our experiments indicate that PRD (using FCOS) outperforms RGB detection by 13.9m AP on LOD-Snow without generating restored images. |
| Researcher Affiliation | Collaboration | 1Xi an Jiaotong University, Xi an, China 2Kargo Bot.ai, Beijing, China 3Tsinghua University, Beijing, China chenhy@stu.xjtu.edu.cn, hungshuotai@didiglobal.com, kaisheng@mail.tsinghua.edu.cn |
| Pseudocode | No | The paper describes methods and includes mathematical formulas but does not contain a clearly labeled 'Pseudocode' or 'Algorithm' block. |
| Open Source Code | Yes | The code is available at https://github.com/Dreamer CCC/Raw Mining. |
| Open Datasets | Yes | To evaluate the real-world performance of low-light detection, we utilize the LOD (Hong et al. 2021) dataset, which consists of 2230 image pairs that are randomly split into a training set of 1830 pairs and a test set of 400 pairs. The RAW-NOD (Morawski et al. 2022) dataset contains 7K raw images captured in outdoor low-light conditions. PASCALRAW (Omid-Zohoor, Ta, and Murmann 2014) contains 4,259 annotated RAW images, with three annotated object classes (car, person, and bicycle), and is modeled after the PASCAL VOC database. |
| Dataset Splits | No | The paper states 'randomly split into a training set of 1830 pairs and a test set of 400 pairs' for the LOD dataset, but does not explicitly mention a validation split with specific numbers or percentages. |
| Hardware Specification | Yes | running on 8 RTX NVIDIA 2080Ti GPUs (12GB). |
| Software Dependencies | No | The paper mentions 'Open MMLab Detection Toolbox (Chen et al. 2019) and PyTorch' but does not specify their version numbers. |
| Experiment Setup | Yes | We follow the official default settings of detectors, e.g. for Center Net, use Random Center Crop Pad and Random Flip as data augmentation. |